“Combining graph databases and LLMs can improve the accuracy of LLM results by 54.2%!”

Neo4j's Stephen Chin believes that a graph is the most intuitive and efficient data structure for storing, searching, and querying data with complex relationships. In a conversation with OSFY’s Yashasvini Razdan, the VP of Developer Relations at Neo4j spoke about the applications of graphical databases in AI and LLM-based solutions...

- Yashasvini Razdan

What industry trends are driving the need for graph technology?

A. There is a major gap between the number of AI applications successfully put into production and those meeting the intended requirements. The primary challenge lies in whether large language models (LLMs) and applications deliver accurate results, avoid hallucinations, and effectively utilise enterprise data and systems. Knowledge graphs bridge this gap by providing additional context and information, avoiding the ‘black box’ problem, and providing a smooth development experience without struggling with models that aren’t well-suited to the task. Research studies have shown that combining graph databases and LLMs can improve the accuracy of LLM results by 50%. This is significant because one of the biggest hurdles to widespread adoption of LLM technology is ensuring accurate, explainable results that can be confidently used in business contexts, especially for mission-critical applications.

By using knowledge graphs, organisations can transition from AI theories and prototypes to building production-ready applications. These applications not only deliver reliable results but also ensure data meets the needs of both internal and external stakeholders with confidence.

So where does open source come into play in all of this?

A. Neo4j has evolved into a highly successful commercial and enterprise database but remains firmly rooted in its open source origins. It is entirely built on open source with an open-sourced database. We have an open source Community Edition, which is free for anyone to use. Without the power of an open source ecosystem, graph technology would never have taken off.

What is the advantage of using open source AI solutions compared to proprietary solutions?